Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

Journal: Epilepsy & behavior : E&B
Published Date: May 30, 2024

Abstract

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a high risk of post-stroke epilepsy. However, there are no reliable methods to accurately predict those at higher risk for developing seizures despite their importance in planning treatments, allocating resources, and advancing post-stroke seizure research. Existing risk models have limitations and have not taken advantage of readily available real-world data and artificial intelligence. This study aims to evaluate the performance of Machine-learning-based models to predict post-stroke seizures at 1 year and 5 years after an intracerebral hemorrhage in unselected patients across multiple healthcare organizations.

Authors

  • Alain Lekoubou
    Department of Neurology, Milton S. Hershey Medical Center and Department of Public Health, Pennsylvania State University, USA. Electronic address: alekouboulooti@pennstatehealth.psu.edu.
  • Justin Petucci
    Institute for Computational and Data Sciences, USA; Clinical and Translational Sciences Institute, USA. Electronic address: jmp579@psu.edu.
  • Temitope Femi Ajala
    Alabama Department of Public Health, USA.
  • Avnish Katoch
    Clinical and Translational Sciences Institute, USA. Electronic address: akatoch@pennstatehealth.psu.edu.
  • Jinpyo Hong
    College of Medicine, Penn State University, Hershey, PA, USA. Electronic address: jhong3@pennstatehealth.psu.edu.
  • Souvik Sen
    University of South Carolina, Department of Neurology, USA. Electronic address: souvik.sen@uscmed.sc.edu.
  • Leonardo Bonilha
    Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA.
  • Vernon M Chinchilli
    Department of Public Health Sciences, Pennsylvania State University, USA. Electronic address: vchinchilli@pennstatehealth.psu.edu.
  • Vasant Honavar
    Institute for Computational and Data Sciences, USA; Clinical and Translational Sciences Institute, USA; Data Sciences Program, USA; College of Information Sciences and Technology, USA; Center for Artificial Intelligence Foundations and Scientific Applications, USA. Electronic address: vuh14@psu.edu.

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